Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy
Surface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy,...
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MDPI AG
2024-10-01
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| Series: | Metals |
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| author | Xuepeng Shan Chaofeng Gao Jeremy Heng Rao Mujie Wu Ming Yan Yunjie Bi |
| author_facet | Xuepeng Shan Chaofeng Gao Jeremy Heng Rao Mujie Wu Ming Yan Yunjie Bi |
| author_sort | Xuepeng Shan |
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| description | Surface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy, AlSi10Mg, was selected to study its surface roughness when using Laser Powder Bed Fusion (LPBF). Two Random Forest (RF) models were established to predict the upper surface roughness of printed samples based on laser power, laser scanning speed, and hatch distance. Through the study, it is found that a two-dimensional (2D) RF model is successful in predicting surface roughness values based on experimental data. The best and minimum surface roughness is 2.98 μm, which is the minimum known without remelting. More than two-thirds of the samples had a surface roughness of less than 7.7 μm. The maximum surface roughness is 11.28 μm. And the coefficient of determination (R<sup>2</sup>) of the model was 0.9, also suggesting that the surface roughness of 3D-printed Al alloys can be predicted using ML approaches such as the RF model. This study helps to understand the relationship between printing parameters and surface roughness and helps print components with better surface quality. |
| format | Article |
| id | doaj-art-b07ef20444c24b2a8f817f03f4d8546c |
| institution | OA Journals |
| issn | 2075-4701 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Metals |
| spelling | doaj-art-b07ef20444c24b2a8f817f03f4d8546c2025-08-20T02:11:15ZengMDPI AGMetals2075-47012024-10-011410114810.3390/met14101148Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al AlloyXuepeng Shan0Chaofeng Gao1Jeremy Heng Rao2Mujie Wu3Ming Yan4Yunjie Bi5Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaJi Hua Laboratory, Institute of Advanced Additive Manufacturing, Foshan 528010, ChinaJi Hua Laboratory, Institute of Advanced Additive Manufacturing, Foshan 528010, ChinaDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaJi Hua Laboratory, Institute of Advanced Additive Manufacturing, Foshan 528010, ChinaSurface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy, AlSi10Mg, was selected to study its surface roughness when using Laser Powder Bed Fusion (LPBF). Two Random Forest (RF) models were established to predict the upper surface roughness of printed samples based on laser power, laser scanning speed, and hatch distance. Through the study, it is found that a two-dimensional (2D) RF model is successful in predicting surface roughness values based on experimental data. The best and minimum surface roughness is 2.98 μm, which is the minimum known without remelting. More than two-thirds of the samples had a surface roughness of less than 7.7 μm. The maximum surface roughness is 11.28 μm. And the coefficient of determination (R<sup>2</sup>) of the model was 0.9, also suggesting that the surface roughness of 3D-printed Al alloys can be predicted using ML approaches such as the RF model. This study helps to understand the relationship between printing parameters and surface roughness and helps print components with better surface quality.https://www.mdpi.com/2075-4701/14/10/1148laser powder bed fusion (3D printing)surface qualitysurface roughnessrandom forestAlSi10Mg |
| spellingShingle | Xuepeng Shan Chaofeng Gao Jeremy Heng Rao Mujie Wu Ming Yan Yunjie Bi Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy Metals laser powder bed fusion (3D printing) surface quality surface roughness random forest AlSi10Mg |
| title | Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy |
| title_full | Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy |
| title_fullStr | Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy |
| title_full_unstemmed | Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy |
| title_short | Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy |
| title_sort | experimental study and random forest machine learning of surface roughness for a typical laser powder bed fusion al alloy |
| topic | laser powder bed fusion (3D printing) surface quality surface roughness random forest AlSi10Mg |
| url | https://www.mdpi.com/2075-4701/14/10/1148 |
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